In the United States, a rolling element bearing burns off from a rail vehicle axle an average of approximately once per week. Many of these incidents result in catastrophic accidents and extensive rail traffic disruption. Other incidents are relatively minor, however, still can temporarily slow or halt rail traffic, resulting in disruption of product delivery. Ultimately, failing bearings can cost many thousands to many millions of dollars for each incident in the rail industry.
Similarly, failure of a wheel bearing on a commercial truck or bus can result in fire or loss of a wheel with significant effects on public safety. Considerable safety and financial value also can be found in the early detection of flaws in bearings for drive shafts for gas turbine engines employed in jet aircraft, electrical power plants, and other industrial operations.
The rail industry has utilized hotbox detectors for an extended period of time to detect overheating bearings and thereby prevent incidents, such as derailment. Each detector includes an infrared sensor that measures the heat associated with a wheel as it passes, and is mounted on the rail or in close proximity to the rail to provide hot wheel data. Unfortunately, hotbox detectors only detect the heat from a bearing that is already well into failure. Some estimates suggest that once a hotbox detector detects a hot bearing, the rail wheel may have only a few miles left before the bearing burns off. To this extent, hotbox detectors are often installed only miles apart along a line to ensure detection prior to an incident occurring.
Regardless, the warning provided by a hotbox detector is often too late to prevent the failure. As a result, whenever a hotbox detector gives an alarm, a train is typically stopped, the bearing physically examined by an engineer, and, if the bearing is indeed hot, the train proceeds at a very low speed (e.g., approximately 2-3 mph) to the nearest siding or spur, where a trackside repair can be carried out. As the nearest siding or spur may be many miles away, this can result in many hours of delay, not just for the affected train, but any other trains traveling on the same line.
Alternative approaches seek to detect faults in rotating components of machinery using vibration data. In these approaches, signal analysis can be applied to the vibration data to determine the presence of a fault. However, these approaches require that the vibration data be acquired by one or more sensors attached to the monitored equipment, which transmits the vibration data to a processing system. Such a configuration is not always desired or possible.
Research has been conducted into the use of acoustic data for evaluating bad wheel bearings. Such research has provided evidence that acoustic data can be used to detect internal roller bearing faults at large standoff distances from operating bearings. In the rail industry, defective railcar roller bearings on freight cars have been detected using acoustic wayside monitoring equipment regardless of whether the freight cars are empty or fully loaded. One approach proposes to use horn-based band pass units and electret microphones as well as the extraction of a signal envelope for use with a hardware band pass filter for the detection of specific frequencies in the acoustic data. Additionally, the use of wheel detectors to determine a likely window within which bearing sounds are expected has been proposed.
In a commercially implemented approach, a twelve microphone array wayside system is used to perform acoustic detection. In this approach, the microphones are spaced approximately three feet apart and each is installed approximately three feet from the rail. To this extent, the system requires a significant amount of data processing (e.g., acoustic data from the twelve microphones) and a significant length of rail (e.g., approximately thirty-two feet). Additionally, the microphones are installed within the distance normally allowed for permanently installed devices, and too close for actual protection of the equipment. As a result, the equipment is frequently damaged by passing trains. Still another approach uses arrays of standard microphones to identify and assess bearing faults. However, this approach also requires that the arrays be placed fairly close to the track, requires multiple passes to detect faulty bearings, and requires installation at every monitored track.
One reason for using multiple linear arrays of microphones to monitor moving objects is to observe the object for a longer period of time. Time slicing of the moving object sound source then can be used to provide an extensive period of recording from the center of the moving object. For faulty railroad bearing detection, it is typically assumed that data for at least two or three revolutions is required to optimize the fault detection signature. As a result, it is argued that the more rotations that are observed, the resulting diagnostic signal will be more accurate. To this extent, it has been noted that the use of a parabolic reflector microphone limits the acoustic capturing arc (e.g., the portion of space in which a given passing signal will be detected by the microphone), which can reduce the effective acoustic bearing scan time.